Methods and apparatus to perform media device asset qualification

ABSTRACT

Methods, apparatus, systems and articles of manufacture to perform media device asset qualification are disclosed. An example apparatus includes an asset quality evaluator to identify candidate media device assets obtained from a media device that identify media. The example apparatus further includes an asset grader to grade the candidate media device assets based on calculating a valid hash count corresponding to a number of matches between a first one of the candidate media device assets compared to a second one of the candidate media device assets using a hash table, and identify the first one of the candidate media device assets as a reference media device asset, the first one having a higher grade compared to grades of other candidate media device assets. The example apparatus further includes an asset loader to generate a validation report including the identification of the reference media device asset.

FIELD OF THE DISCLOSURE

This disclosure relates generally to monitoring media and, moreparticularly, to methods and apparatus to perform media device assetqualification.

BACKGROUND

In recent years, methods of accessing media have evolved. For example,in the past, media was primarily accessed via televisions coupled toset-top boxes. Recently, media services deployed via Over-The-Top (OTT)devices or internet streaming capable devices, such as an Amazon KindleFire™, an Apple TV®, a Roku® media player, etc., have been introducedthat allow users to request and present the media on the OTT devices.Such OTT devices, as well as other media presentation platforms, such asdesktop, laptop, and handheld mobile devices (e.g., smartphones,tablets, etc.) enable consumption of the media from a variety of contentproviders and content publishers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in which an examplemedia device asset manager monitors media from media devices.

FIG. 2 is a block diagram of an example implementation of the mediadevice asset manager of FIG. 1.

FIG. 3 is a schematic illustration of an example asset quality evaluatorprocessing media device assets obtained from the media devices of FIG.1.

FIG. 4 is a schematic illustration of an example asset hasher processingthe media device assets of FIG. 3 obtained from the media devices ofFIG. 1.

FIG. 5 is a schematic illustration of an example asset matcherprocessing the media device assets of FIG. 3 obtained from the mediadevices of FIG. 1.

FIGS. 6-8 are flowcharts representative of example methods that may beused to implement the example media device asset manager of FIGS. 1-2.

FIG. 9 is a block diagram of an example processing platform structuredto execute the example machine readable instructions of FIGS. 6-8 toimplement the media device asset manager of FIGS. 1 and 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Many entities have an interest in understanding how users are exposed tomedia on the Internet. For example, an audience measurement entity (AME)desires knowledge on how users interact with media devices such assmartphones, tablets, laptops, smart televisions, etc. In particular, anexample AME may want to monitor media presentations made at the mediadevices to, among other things, monitor exposure to advertisements,determine advertisement effectiveness, determine user behavior, identifypurchasing behavior associated with various demographics, etc.

AMEs coordinate with advertisers to obtain knowledge regarding anaudience of media. For example, advertisers are interested in knowingthe composition, engagement, size, etc. of an audience for media. Forexample, media (e.g., audio and/or video media) may be distributed by amedia distributor to media consumers. Content distributors, advertisers,content producers, etc. have an interest in knowing the size of anaudience for media from the media distributor, the extent to which anaudience consumes the media, whether the audience pauses, rewinds, fastforwards the media, etc. As used herein the term “content” includesprograms, advertisements, clips, shows, etc. As used herein, the term“media” includes any type of content and/or advertisement delivered viaany type of distribution medium. As used herein “media” refers to audioand/or visual (still or moving) content and/or advertisements. Thus,media includes television programming or advertisements, radioprogramming or advertisements, movies, web sites, streaming media, etc.

In some instances, AMEs identify media by extracting media identifierssuch as signatures or media-identifying metadata such as codes,watermarks, etc., and comparing them to reference media identifiers. Forexample, fingerprint or signature-based media monitoring techniquesgenerally use one or more inherent characteristics of the monitoredmedia during a monitoring time interval to generate a substantiallyunique proxy for the media. Such a proxy is referred to as a signatureor fingerprint, and can take any form (e.g., a series of digital values,a waveform, etc.) representative of any aspect(s) of the media signal(s)(e.g., the audio and/or video signals forming the media presentationbeing monitored). A signature may be a series of signatures collected inseries over a timer interval. A good signature is repeatable whenprocessing the same media presentation, but is unique relative to other(e.g., different) presentations of other (e.g., different) media.Accordingly, the term “fingerprint” and “signature” are usedinterchangeably herein and are defined herein to mean a proxy foridentifying media that is generated from one or more inherentcharacteristics of the media.

In some instances, an unrepeatable signature or an unmatchable signaturefor the media may be generated due to background noise or mediapresentation environmental noise. For example, while generating areference signature, an audible noise emanating from the media device(e.g., a noise from a message alert on a smartphone, a noise from anemail alert on a tablet, etc.) while the media device is presenting themedia can cause undesired audio characteristics to be included in thereference signature. In such an example, when comparing a signaturecorresponding to the media to the reference signature corresponding tothe same media, a match may not be made due to the audible noisecharacteristics included in the reference signature.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

Example methods, apparatus, and articles of manufacture disclosed hereinmonitor media presentations at media devices. Such media devices mayinclude, for example, Internet-enabled televisions, personal computers(e.g., desktop computers, laptop computers, etc.), Internet-enabledmobile handsets (e.g., a smartphone), video game consoles (e.g., Xbox®,PlayStation®), tablet computers (e.g., an iPad®), digital media players(e.g., an Apple TV®, an Amazon Kindle Fire™, a Roku® media player, aSlingbox®, etc.), etc.

In examples disclosed herein, a media device asset manager (MDAM)obtains a media device asset including one or more signatures from amedia device and one or more corresponding media identifiers. As usedherein, the term “media device asset” refers to any type of extractedinformation from media presented at a media device that includes one ormore signatures or media-identifying metadata such as one or more codes,one or more watermarks, etc. As used herein, the term “media identifier”refers to any type of media identification information that includes asource identifier, a stream identifier, a passive audio signature (PAS)timestamp, a duration of media, etc., and/or a combination thereof.

In some disclosed examples, the MDAM obtains a media asset including oneor more signatures and one or more corresponding media identifiers notfrom a media device. As used herein, the term “media asset” refers toany type of extracted information from media that includes one or moresignatures or media-identifying metadata such as one or more codes, oneor more watermarks, etc.

In some disclosed examples, a media device asset is a collection of twoor more signatures from a media device that individually and/orcollectively identifies media from which the signatures were obtained.For example, the media device asset may be a sequence of two or moresignatures obtained from a meter operating on an Over-The-Top (OTT)device monitoring a presentation of the Home Box Office (HBO) content“Game of Thrones” on the OTT device. In another example, the meter maybe operating externally to the OTT device. In such an example, the mediadevice asset may be a sequence of two or more signatures obtained from amedia meter, a people meter, etc., monitoring a presentation of themedia.

In some disclosed examples, media is presented at a media device and ameter monitoring the media device uses signature-generation software togenerate media device assets based on the presented media. In suchdisclosed examples, the media device asset may include unidentifiabledata or unmatchable data (e.g., unidentifiable signatures, etc.) due toenvironmental elements such as audible noise emanating from the mediadevice (e.g., a noise from a message alert on a smartphone, a noise froman email alert on a tablet, etc.). In some disclosed examples, aqualification process can be applied to the unidentifiable signatures todetermine whether they can be stored in a reference signature database.In some disclosed examples, the meter operates on the media device(e.g., a signature-generation application executing computer readableinstructions on a laptop, etc.). In other disclosed examples, the meteroperates externally to the media device (e.g., a standalone meteringdevice, etc.).

In some disclosed examples, the MDAM determines whether an obtainedmedia device asset is a duplicate syndicated media device asset, aduplicate proprietary media asset, or a syndicated duplicate of aproprietary media asset. As used herein, the term “syndicated mediadevice asset” refers to a media device asset obtained from a mediadevice that can be subsequently used for measurement and/or reportingfor any AME client. As used herein, the term “proprietary media asset”refers to a media device asset obtained from a client of the AME and mayonly be subsequently used for measurement and/or reporting for theclient.

In some disclosed examples, the MDAM determines that a media deviceasset obtained from a media device has already been stored in a database(e.g., a media device asset database, etc.). For example, the MDAM mayidentify the media device asset as a duplicate syndicated media deviceasset. In such an example, the MDAM may (1) identify the media deviceasset based on an extracted media identifier, (2) determine that themedia device asset has previously been stored in the database, and (3)determine that the previously stored media device asset is not aproprietary media asset. In such an example, the MDAM may store a logcorresponding to determining that the media device asset is a duplicatesyndicated media device asset. Additionally or alternatively, theexample MDAM may increment a duplicate syndicated media device assetcounter corresponding to a number of times the media device asset isobtained and/or determined to be a duplicate syndicated media deviceasset. In response to storing the log and/or incrementing the duplicatesyndicated media device asset counter, the MDAM may discard the mediadevice asset.

In some disclosed examples, the MDAM determines that an obtained mediaasset is a duplicate proprietary media asset. In such an example, theMDAM may store a log corresponding to determining that the obtainedmedia device asset is a duplicate proprietary media asset. Additionallyor alternatively, the example MDAM may increment a duplicate proprietarymedia asset counter corresponding to a number of times the media deviceasset is obtained and/or determined to be a duplicate proprietary mediaasset. In response to storing the log and/or incrementing the duplicateproprietary media asset counter, the MDAM may discard the obtained mediaasset.

In some disclosed examples, the MDAM identifies a media device assetobtained from a media device as a syndicated duplicate of a proprietarymedia asset. In such an example, the MDAM may (1) identify the mediadevice asset based on an extracted media identifier, (2) determine thatthe media device asset has previously been stored in the database, and(3) determine that the previously stored media device asset is aproprietary media asset. In such an example, the MDAM may store a logcorresponding to determining that the media device asset is a syndicatedduplicate of a proprietary media asset. Additionally or alternatively,the example MDAM may replace the previously stored proprietary mediaasset with the media device asset.

In some disclosed examples, the MDAM determines that a media deviceasset obtained from a media device has not been previously stored in adatabase (e.g., a media device asset database, etc.). In such disclosedexamples, the MDAM identifies the media device asset as a databasecandidate. For example, a database candidate may correspond to mediawhere there are no reference signatures stored in the database. As aresult, a qualification process can be applied to one or more databasecandidates to determine a best one of the one or more databasecandidates to be stored in the database as a reference signature, areference media device asset, etc.

As used herein, the term “database candidate” refers to a media deviceasset (e.g., a candidate media device asset, etc.) that can be selectedto be stored in a database (e.g., a media device asset database, etc.)for AME measurement and/or reporting. For example, the MDAM may (1)obtain a media device asset, (2) identify media corresponding to themedia device asset based on an extracted media identifier, (3) determinethat the media device asset has not been previously stored in a mediadevice asset database, (4) identify the media device asset as a databasecandidate, and (5) generate a database candidate counter. In such anexample, the MDAM may (1) increment the database candidate counter eachtime a media device asset corresponding to the media is obtained, and(2) store the database candidate in a temporary database.

In some disclosed examples, the MDAM compares the database candidatecounter to a threshold (e.g., a counter value of 10, 100, 1000, etc.)and determines whether the database candidate counter satisfies thethreshold (e.g., a value of the database candidate counter is greaterthan 10, 100, 1000, etc.). In response to determining that the databasecandidate counter satisfies the threshold, the example MDAM may performa qualification process on the one or more database candidates toidentify which one of the database candidates is the best candidate tobe stored in the media device asset database as a reference signature, areference media device asset, etc., to be used by the AME formeasurement and/or reporting, etc.

In some disclosed examples, the MDAM identifies a database candidate tobe stored in a media device asset database by processing each mediadevice asset based on continuity, commonality, and completeness. Forexample, the MDAM may process a media device asset for continuityanomalies that indicate trick mode, jumps in PAS timestamps, etc. Insuch an example, the MDAM may determine that a media device assetindicates trick mode based on the media-identifying metadata in themedia identifier. In another example, the MDAM may determine that a userperformed a viewing operation such as pausing, rewinding, fastforwarding, etc., based on a jump in PAS timestamps between signatures,the extracted media identifier, etc. In some disclosed examples, theMDAM discards a media device asset that is identified as a disqualifiedmedia asset, includes continuity anomalies, etc.

In another example, the MDAM may process the database candidates basedon commonality by generating a hash table. For example, the MDAM mayapply a hashing algorithm to the database candidates to generate a hashtable. In such an example, the MDAM may compare (e.g., iterativelycompare, etc.) each database candidate to another database candidateusing the hash table. In some disclosed examples, the MDAM grades theresults from the comparison process based on criterion such as validhash counts, duration, gaps, etc. In such disclosed examples, the MDAMidentifies the database candidate as a reference media device assetbased on the grading process (e.g., the database candidate with thehighest grade is selected, etc.). In such disclosed examples, the MDAMprocesses the reference media device asset by trimming, cropping, etc.,non-matching portions of the reference media device asset. In suchdisclosed examples, the MDAM stores the reference media device assetinto a database (e.g., the media device asset database, etc.). Inanother example, the MDAM may process the database candidates forcompleteness by determining if one or more PAS timestamps are missingfrom a media device asset, if the media device asset satisfies a minimumduration threshold, etc.

FIG. 1 is a block diagram of an example environment 100 constructed inaccordance with the teachings of this disclosure to identify mediapresented at a media device. The example environment 100 includesexample first, second, and third media devices 102, 104, 106. In theillustrated example of FIG. 1, the media devices 102, 104, 106 aredevices that obtain media 108 and present the media 108. In theillustrated example, the media 108 is a video that includes audio.Alternatively, any other type of media may be used. In some examples,the media devices 102, 104, 106 are capable of directly presenting media(e.g., via a display) while, in some other examples, the media devicespresent the media on separate media presentation equipment (e.g.,speakers, a display, etc.). For example, the media device 102 of theillustrated example is an Internet-enabled television capable ofpresenting media (e.g., via an integrated display and speakers, etc.)streaming from an OTT device. Alternatively, the media device 102 may beany other type of media device. Further, while in the illustratedexample three media devices are shown, any number of media devices maybe used.

In the illustrated example of FIG. 1, each of the media devices 102,104, 106 include a meter 110. In the illustrated example, the meter 110is a software application operating on the media devices 102, 104, 106executing computer readable instructions to generate media deviceassets. Additionally or alternatively, the meter 110 may operateexternally to the media devices 102, 104, 106 (e.g., a standalone deviceincluding a processor executing computer readable instructions, etc.).In the illustrated example, the meter 110 generates a media device asset112 based on the media 108. In the illustrated example, the media deviceasset 112 includes a signature 114 and a media identifier 116. In theillustrated example, the signature 114 includes one or more audio-basedsignatures. Alternatively, the signature 114 may include one or morevideo-based signatures and/or any other type of signature based on mediaidentification information (e.g., media-identifying metadata, etc.). Inthe illustrated example, the media identifier 116 includesmedia-identifying metadata corresponding to the media 108. For example,the meter 110 may determine that the signature 114 corresponds to thepresentation of Season 7 Episode 1 of “Game of Thrones” based onanalyzing the media-identifying metadata stored in the media identifier116, where the media-identifying metadata was extracted from the audioof the media 108.

In the illustrated example of FIG. 1, the meter 110 transmits the mediadevice asset 112 to a media device asset manager (MDAM) 118 via anetwork 120. In the illustrated example of FIG. 1, the network 120 isthe Internet. However, the example network 120 may be implemented usingany suitable wired and/or wireless network(s) including, for example,one or more data buses, one or more Local Area Networks (LANs), one ormore wireless LANs, one or more cellular networks, one or more privatenetworks, one or more public networks, etc. The example network 120enables the media devices 102, 104, 106, the meter 110, etc., to be incommunication with the MDAM 118. As used herein, the phrase “incommunication,” including variances (e.g., secure or non-securecommunications, compressed or non-compressed communications, etc.)therefore, encompasses direct communication and/or indirectcommunication through one or more intermediary components and does notrequire direct physical (e.g., wired) communication and/or constantcommunication, but rather includes selective communication at periodicor aperiodic intervals, as well as one-time events.

In the illustrated example of FIG. 1, the MDAM 118 coordinates anidentification, a selection, etc., of a media device asset to be storedin a database for measuring and/or reporting by an AME. For example, theMDAM 118 may identify the media device asset 112 as a databasecandidate. In such an example, the MDAM 118 may apply a hashingalgorithm to one or more media device assets including the media deviceasset 112 to generate a hash table, compare the media device asset 112to one or more other media device assets based on the hash table, anddetermine whether to store the media device asset 112 in the databasebased on the comparison.

In the illustrated example of FIG. 1, a report generator 122 generatesand/or prepares reports using information stored in the media deviceasset database. In the illustrated example, the report generator 122prepares media measurement reports indicative of the exposure of themedia 108 on the media devices 102, 104, 106. In some examples, thereport generator 122 generates a report identifying demographicsassociated with the media 108 based on identifying one or more mediadevice assets including the media device asset 112. For example, apanelist at a media exposure measurement location may have provided thepanelist's demographics to the AME. The report generator 122 may preparea report associating the obtained panelist demographics with the media108.

FIG. 2 is a block diagram of an example implementation of the exampleMDAM 118 of FIG. 1. The example MDAM 118 manages a media device assetdatabase based on identifying media device assets obtained from mediadevices as database candidates and selecting one of the databasecandidates for storage in the media device asset database and subsequentmeasuring and/or monitoring by an AME. In the illustrated example ofFIG. 2, the example MDAM 118 includes an example network interface 200,an example asset quality evaluator 210, an example asset hasher 220, anexample asset matcher 230, an example asset grader 240, an example assetloader 250, and an example database 260.

In the illustrated example of FIG. 2, the MDAM 118 includes the networkinterface 200 to obtain information from and/or transmit information tothe network 120 of FIG. 1. In the illustrated example, the networkinterface 200 implements a web server that receives the media deviceasset 112 from the media device 102 and/or the meter 110. In theillustrated example, the information included in the media device asset112 is formatted as an HTTP message. However, any other message formatand/or protocol may additionally or alternatively be used such as, forexample, a file transfer protocol (FTP), a simple message transferprotocol (SMTP), an HTTP secure (HTTPS) protocol, etc. In some examples,the network interface 200 determines whether to continue monitoring amedia device. For example, the network interface 200 may determine thatthe media devices 102, 104, 106 of FIG. 1 are not presenting the media108 of FIG. 1, are not powered on, etc.

In the illustrated example of FIG. 2, the MDAM 118 includes the assetquality evaluator 210 to identify one or more media device assets as adatabase candidate. For example, the asset quality evaluator 210 maydetermine that the media device asset 112 of FIG. 1 is a duplicatesyndicated media device asset, a duplicate proprietary media asset, or asyndicated duplicate of a proprietary media asset. For example, theasset quality evaluator 210 may increment a counter (e.g., a duplicatesyndicated media device asset counter, etc.) corresponding to asyndicated media device asset when the media device asset is identifiedas a duplicate syndicated media device asset. In another example, theasset quality evaluator 210 may increment a counter (e.g., a duplicateproprietary media asset counter, etc.) corresponding to a proprietarymedia asset when the media device asset is identified as a duplicateproprietary media asset. In some examples, the asset quality evaluator210 discards the media device asset when the media device asset isidentified as a duplicate syndicated media device asset or a duplicateproprietary media asset. In some examples, the asset quality evaluator210 replaces a stored proprietary media asset with the media deviceasset when the media device asset is determined to be a syndicatedduplicate of the stored proprietary media asset.

In some examples, the asset quality evaluator 210 analyzes the mediadevice asset 112 for continuity anomalies based on an extracted mediaidentifier. For example, the asset quality evaluator 210 may identify anexpected duration of the media 108 based on the media identifier 116.For example, the asset quality evaluator 210 may determine that themedia 108 has an expected media presentation duration of 59 minutes and36 seconds (i.e., 59.6 minutes). In some examples, the asset qualityevaluator 210 composes an ideal database candidate based on the expectedduration and an expected number of signatures for the expected duration.For example, the asset quality evaluator 210 may determine that theexpected number of signatures for the media 108 is 35,740 (e.g., 59.6minutes×600 signatures per minute=35, 740 signatures), where a signatureis to occur once every 100 milliseconds.

In some examples, the asset quality evaluator 210 compares the mediadevice asset 112 of FIG. 1 to the ideal database candidate. For example,the asset quality evaluator 210 may determine that the media deviceasset 112 has a longer duration than the expected duration and, thus,indicating that the media device asset 112 includes pauses, rewinds,etc., of the media 108. In such an example, the asset quality evaluator210 may identify the media device asset 112 as a disqualified mediadevice asset. In some examples, the asset quality evaluator 210 discardsidentified disqualified media device assets.

In another example, the asset quality evaluator 210 may determine thatthe media device asset 112 has a shorter duration than the expectedduration. For example, a presentation of the media 108 may have beenstopped, fast forwarded, etc. In some examples, the asset qualityevaluator 210 calculates a difference (e.g., a duration difference,etc.) between the duration of the media device asset 112 and theexpected duration, compares the difference to a threshold, anddetermines whether the difference satisfies the threshold based on thecomparison (e.g., the difference is greater than 60 seconds, 120seconds, etc.). For example, the asset quality evaluator 210 maydetermine that the difference satisfies the threshold based on thedifference being greater than 120 seconds. As a result, the assetquality evaluator 210 may discard the media device asset 112 based onthe media device asset 112 having a high probability that a plurality ofsignatures 114 are missing from the media device asset 112. In anotherexample, the asset quality evaluator 210 may discard the media deviceasset 112 based on the signatures 114 corresponding to a differentlanguage (e.g., English, French, German, etc.) than other media deviceassets being analyzed. For example, the asset quality evaluator 210 maydisqualify the media device asset 112 for including French-basedsignatures when the language to be processed is English.

In some examples, the asset quality evaluator 210 selects a media deviceasset to process. For example, the asset quality evaluator 210 mayselect the media device asset 112 of FIG. 1 to analyze for continuityanomalies. In some instances, the asset quality evaluator 210 determineswhether there is another media device asset to process. For example, theasset quality evaluator 210 may determine that there are additionalmedia device assets from the media devices 102, 104, 106 that have notbeen processed.

In some examples, the asset quality evaluator 210 selects a timestamp ofinterest to process in a media device asset. For example, the assetquality evaluator 210 may select a first of ten timestamps to processwithin the media device asset 112. In such an example, the asset qualityevaluator 210 may process a first timestamp corresponding to a firstsignature to detect a jump in PAS timestamps between the first timestampand a second timestamp. In some instances, the asset quality evaluator210 determines whether there is another timestamp to process in themedia device asset.

In the illustrated example of FIG. 2, the MDAM 118 includes the assethasher 220 to generate a hash table based on applying one or morehashing algorithms to one or more database candidates (e.g., one or moremedia device assets, etc.). In some examples, a hash table is a datastructure which implements an associative array abstract data type,which is a structure that can map keys to values. In some instances, theasset hasher 220 generates a hash table by using a hash function tocompute an index into an array of buckets or slots, from which thedesired value can be found. The example asset hasher 220 may implement ahash function such as a message digest 5 hash function, a secure hashalgorithm (SHA) (e.g., SHA-0, SHA-1, SHA-2, etc.), etc. For example, theasset hasher 220 may use a SHA-0 hash algorithm to map each signature114 of the media device asset 112 of FIG. 1 to a slot that includes themedia identifier 116 corresponding to the signature 114.

In the illustrated example of FIG. 2, the MDAM 118 includes the assetmatcher 230 to perform database candidate to database candidate matchingbased on the hash table. For example, the asset matcher 230 may comparethe media device asset 112 of FIG. 1 to another media device asset usinga hash table generated by the asset hasher 220. In such an example, theasset matcher 230 may compare each signature 114 of the media deviceasset 112 to the hash table. For example, the asset matcher 230 may (1)apply a hashing algorithm (e.g., the same hashing algorithm used by theasset hasher 220 to generate the hash table, etc.) to the signature 114to compute an index value and (2) compare the media identifier 116corresponding to the signature 114 to a media identifier stored at theindex value in the hash table. In such an example, the asset matcher 230may determine that the media identifier 116 either matches or does notmatch the stored media identifier based on the comparison.

In some examples, the asset matcher 230 calculates a matching percentagecorresponding to how well the media device asset 112 matches anotherdatabase candidate based on the generated hash table. For example, theasset matcher 230 may calculate a matching percentage corresponding tohow many signatures of the media device asset 112 have media identifiers116 that match stored media identifiers in the generated hash table. Inanother example, the asset matcher 230 may calculate a match count(e.g., a hash count, etc.) corresponding to a number of the signatures114 that have media identifiers 116 that match stored media identifiersin the generated hash table.

In the illustrated example of FIG. 2, the MDAM 118 includes the assetgrader 240 to generate a grade for one or more database candidates. Forexample, the asset grader 240 may grade the media device asset 112 basedon a strength of coverage. In such an example, the asset grader 240 maydetermine a grade for the media device asset 112 based on a valid hashcount, a number of discrete hash matches, etc. The example asset grader240 may assign a higher grader based on an increasing number of thevalid hash count, the number of discrete hash matches, etc. In anotherexample, the asset grader 240 may grade the media device asset 112 basedon a duration of coverage. In such an example, the asset grader 240 maydetermine a grade for the media device 112 based on a duration of thesignatures 114 compared to an expected duration of the media 108.

In yet another example, the asset grader 240 may determine a grade forthe media device asset 112 based on a minimum duration coverage. In suchan example, the asset grader 240 may fail to assign a grade to the mediadevice asset 112 if a first duration difference between the duration ofthe signatures 114 and the expected duration satisfies a threshold(e.g., a duration difference greater than 60 seconds, 5 minutes, 10minutes, etc.). For example, the asset grader 240 may fail to assign agrade to the media device asset 112 if the first duration difference isgreater than 10 minutes. Alternatively, the example asset grader 240 mayassign a lower grade to the media device asset 112 when the firstduration difference satisfies the threshold compared to a grade assignedby the asset grader 240 to another media device asset with acorresponding second duration difference that does not satisfy thethreshold. In some examples, the asset grader 240 selects a databasecandidate to be loaded into a database (e.g., a media device assetdatabase) based on the grades. In such examples, the asset grader 240identifies the selected database candidate as a reference media deviceasset. For example, the asset grader 240 may select the media deviceasset 112 to be loaded into a media device asset database based onassigning the media device asset 112 the highest grade compared to othermedia device assets.

In the illustrated example of FIG. 2, the MDAM 118 includes the assetloader 250 to process and store an identified database candidate (e.g.,an identified signature, a reference media device asset, etc.) in adatabase. In some examples, the asset loader 250 processes theidentified database candidate by trimming, cropping, etc., non-matchingportions of the identified database candidate. For example, the assetloader 250 may remove the signature 114 included in the media deviceasset 112 that does not match the generated hash table. In someexamples, the asset loader 250 stores the processed identified databasecandidate in the database 260 to be used by an AME for measuring and/orreporting operations corresponding to the media 108 of FIG. 1. In someexamples, the asset loader 250 generates a report (e.g., a validationreport) indicating the identified database candidate and a storage ofthe identified database candidate into the database 260.

In the illustrated example of FIG. 2, the MDAM 118 includes the database260 to record data (e.g., media device assets, hash tables, mediaidentification information, matching percentages, grades, rankings,etc.). In the illustrated example, the database 260 is a media deviceasset database. Alternatively, the example database 260 may be any othertype of database. The example database 260 may be implemented by avolatile memory (e.g., a Synchronous Dynamic Random Access Memory(SDRAM), Dynamic Random Access Memory (DRAM), RAMBUS Dynamic RandomAccess Memory (RDRAM), etc.) and/or a non-volatile memory (e.g., flashmemory). The database 260 may additionally or alternatively beimplemented by one or more double data rate (DDR) memories, such as DDR,DDR2, DDR3, DDR4, mobile DDR (mDDR), etc. The example database 260 mayadditionally or alternatively be implemented by one or more mass storagedevices such as hard disk drive(s), compact disk drive(s), digitalversatile disk drive(s), solid-state disk drive(s), etc. While in theillustrated example the database 260 is illustrated as a singledatabase, the database 260 may be implemented by any number and/ortype(s) of databases. Furthermore, the data stored in the database 260may be in any data format such as, for example, binary data, commadelimited data, tab delimited data, structured query language (SQL)structures, etc. Alternatively, the example database 260 may be locatedexternally to the MDAM 118.

While an example manner of implementing the MDAM 118 of FIG. 1 isillustrated in FIG. 2, one or more of the elements, processes and/ordevices illustrated in FIG. 2 may be combined, divided, re-arranged,omitted, eliminated and/or implemented in any other way. Further, theexample network interface 200, the example asset quality evaluator 210,the example asset hasher 220, the example asset matcher 230, the exampleasset grader 240, the example asset loader 250, the example database 260and/or, more generally, the example MDAM 118 of FIG. 2 may beimplemented by hardware, software, firmware and/or any combination ofhardware, software and/or firmware. Thus, for example, any of theexample network interface 200, the example asset quality evaluator 210,the example asset hasher 220, the example asset matcher 230, the exampleasset grader 240, the example asset loader 250, the example database 260and/or, more generally, the example MDAM 118 could be implemented by oneor more analog or digital circuit(s), logic circuits, programmableprocessor(s), application specific integrated circuit(s) (ASIC(s)),programmable logic device(s) (PLD(s)) and/or field programmable logicdevice(s) (FPLD(s)). When reading any of the apparatus or system claimsof this patent to cover a purely software and/or firmwareimplementation, at least one of the example network interface 200, theexample asset quality evaluator 210, the example asset hasher 220, theexample asset matcher 230, the example asset grader 240, the exampleasset loader 250, and/or the example database 260 is/are herebyexpressly defined to include a non-transitory computer readable storagedevice or storage disk such as a memory, a digital versatile disk (DVD),a compact disk (CD), a Blu-ray disk, etc. including the software and/orfirmware. Further still, the example MDAM 118 of FIG. 1 may include oneor more elements, processes and/or devices in addition to, or insteadof, those illustrated in FIG. 2, and/or may include more than one of anyor all of the illustrated elements, processes and devices.

FIG. 3 is a schematic illustration of the example asset qualityevaluator 210 of FIG. 2 processing media device assets obtained from themedia devices 102, 104, 106 of FIG. 1 for continuity anomalies. In theillustrated example, the asset quality evaluator 210 generates an idealmedia device asset (MDA) 300 and compares the ideal MDA to a firstthrough a fifth MDA 302, 304, 306, 308, 310. In the illustrated example,the first through the fifth MDAs 302, 304, 306, 308, 310 are obtainedfrom the media devices 102, 104, 106 of FIG. 1. For example, the MDA 1302 may correspond to the media device asset 112 of FIG. 1.

In the illustrated example of FIG. 3, the asset quality evaluator 210composes the ideal MDA 300 based on an estimated duration of mediaincluded in a media identifier corresponding to the media. For example,the asset quality evaluator 210 may identify an estimated duration ofthe media 108 based on information included in the media identifier 116.In the illustrated example, the asset quality evaluator 210 determinesthat the expected duration of the media 108 corresponding to the firstthrough the fifth MDAs 302, 304, 306, 308, 310 is ten time units,designated by t=0 to t=10. In the illustrated example, the asset qualityevaluator 210 determines that the number of expected signatures for theduration of the media is ten signatures, where the first through thetenth expected signatures and corresponding media identifiers of theideal MDA 300 are designated by S1, S2, S3, S4, S5, S6, S7, S8, S9, andS10. In the illustrated example, the MDA 1 302 has a duration of eighttime units, the MDA 2 304 has a duration of five time units, the MDA 3306 has a duration of seven time units, the MDA 4 308 has a duration ofnine time units, and the MDA 5 310 has a duration of four time units.

In the illustrated example, the MDA 1 302 includes eight signaturesdesignated by A1 through A8 and eight corresponding media identifiersdesignated by AA1 through AA8. For example, A1 may correspond to thesignature 114 and AA1 may correspond to the media identifier 116 of FIG.1, where the media identifier 116 includes a corresponding PAStimestamp. In the illustrated example, the MDA 2 304 includes fivesignatures designated by B1 through B5 and five corresponding mediaidentifiers designated by BB1 through BB5. In the illustrated example,the MDA 3 306 includes seven signatures designated by C1 through C7 andseven corresponding media identifiers designated by CC1 through CC7. Inthe illustrated example, the MDA 4 308 includes nine signaturesdesignated by D1 through D9 and nine corresponding media identifiersdesignated by DD1 through DD9. In the illustrated example, the MDA 5 310includes four signatures designated by E1 through E4 and fourcorresponding media identifiers designated by EE1 through EE4.

In the illustrated example, the asset quality evaluator 210 processeseach of the MDAs 302, 304, 306, 308, 310 for continuity anomalies. Forexample, the asset quality evaluator 210 may compare a PAS timestamp ofeach signature to a PAS timestamp of a preceding signature and/or a PAStimestamp of a following signature. In the illustrated example, theasset quality evaluator 210 compares the fourth signature B4 of the MDA2 304 to the third signature B3 of the MDA 2 304. For example, the assetquality evaluator 210 may determine that the fourth signature B4 of theMDA 2 304 has a PAS timestamp that precedes a PAS timestamp of the thirdsignature B3 of the MDA 2 304. In such an example, the asset qualityevaluator 210 may determine that the fourth signature B4 of the MDA 2304 indicates a jump in PAS timestamps has occurred. In response todetermining that there is a jump in the PAS timestamps of the MDA 2 304,the example asset quality evaluator 210 may identify the MDA 2 304 as adisqualified MDA. As a result, the example asset quality evaluator 210may discard the MDA 2 304.

In some examples, the asset quality evaluator 210 discards an MDA basedon a duration of the MDA. For example, the asset quality evaluator 210may calculate a duration difference between a duration of the ideal MDA300 and a duration of the MDA 5 310. In such an example, the assetquality evaluator 210 may compare the duration difference to a thresholdand determine whether the duration difference satisfies the thresholdbased on the comparison. In the illustrated example, the asset qualityevaluator 210 calculates a duration difference between the ideal MDA 300and the MDA 5 310 to be six time units (e.g., ten time units (i.e.,t=10)−four time units (i.e., t=4), etc.). In the illustrated example,the asset quality evaluator 210 determines that the calculated durationdifference of six time units is greater than the threshold of two timeunits and, thus, satisfies the threshold. In response to the calculatedduration difference satisfying the threshold, the example asset qualityevaluator 210 may identify the MDA 5 310 as a disqualified MDA. As aresult, the example asset quality evaluator 210 may discard the MDA 5310. In response to discarding the MDA 2 304 and the MDA 5 310, theexample asset quality evaluator 210 may identify the MDA 1 302, the MDA3 306, and the MDA 4 308 as database candidates.

In some examples, the asset quality evaluator 210 identifies adisqualified MDA based on a duration of an MDA. For example, the assetquality evaluator 210 may compare a duration of the MDA 5 310 to athreshold (e.g., a duration greater than five time units, seven timeunits, etc.) and determine whether the duration satisfies the threshold.In the illustrated example, the asset quality evaluator 210 identifiesthe MDA 5 310 as a disqualified MDA based on the duration of the MDA 5310 of four time units not satisfying an example threshold of six timeunits.

FIG. 4 is a schematic illustration of the example asset hasher 220 ofFIG. 2 generating a hash table 400 based on performing one or morehashing operations on the media device asset (MDA) 1 302, the MDA 3 306,and the MDA 4 308 of FIG. 3. In the illustrated example, the MDA 1 302,the MDA 3 306, and the MDA 4 308 are identified as database candidates.For example, the asset quality evaluator 210 may identify the MDA 1 302,the MDA 3 306, and the MDA 4 308 as database candidates based on notbeing identified as a disqualified MDA candidate (e.g., does not includea continuity anomaly, a duration difference does not satisfy athreshold, a duration satisfies a threshold, etc.).

In the illustrated example of FIG. 4, the example asset hasher 220applies one or more hashing algorithms, operations, etc., to eachsignature of the MDA 1 302, the MDA 3 306, and the MDA 4 308. Forexample, the asset hasher 220 may apply a hashing algorithm to the firstsignature A1, the second signature A2, the third signature A3, thefourth signature A4, the fifth signature A5, the sixth signature A6, theseventh signature A7, and the eighth signature A8 of the MDA 1 302. Insuch an example, the asset hasher 220 may map each of the signatures toan index in the hash table 400. In the illustrated example, the assethasher 220 maps the first signature A1 of the MDA 1 302 to index 00 andstores the corresponding media identifier AA1 of the first signature A1at the index 00.

In the illustrated example, the asset hasher 220 generates the hashtable 400 to include more indices than signatures. For example, theasset hasher 220 may generate the hash table 400 with a significantnumber of indices greater than a possible number of signatures (e.g.,generate a table with 1,000,000 indices compared to a possible number of1,000 signatures, etc.) to reduce a probability of a collision event. Insome examples, the asset hasher 220 implements one or more collisionresolution algorithms when generating the hash table 400.

FIG. 5 is a schematic illustration of the example asset matcher 230 ofFIG. 2 comparing the MDA 1 302, the MDA 3 306, and the MDA 4 308 of FIG.3 to each other based on the hash table 400 of FIG. 4. In theillustrated example, the asset matcher 230 compares each of the MDAs302, 306, 308 to the hash table 400. For example, the asset matcher 230compares (e.g., iteratively compares, etc.) each signature in each ofthe MDAs 302, 306, 308 to the hash table 400 and calculates a matchcount based on the comparison as shown in a match count table 500.

In the illustrated example, the asset matcher 230 compares the firstthrough the eighth signatures A1-A8 of the MDA 1 302 to the hash table400. For example, the asset matcher 230 applies a hashing algorithm tothe first signature A1 of the MDA 1 302 and maps the first signature tothe index 00. In the illustrated example, the asset matcher 230determines that the index 00 includes the media identifiers AA1, CC1,and DD1. As a result, the example asset matcher 230 determines that thefirst signature A1 of the MDA 1 302 also matches the first signature C1of the MDA 3 306 and the first signature D1 of the MDA 4 308. In such anexample, the first signature A1 of the MDA 1 302 matching the firstsignature C1 of the MDA 3 306 represents a valid hash count.

In the illustrated example, the asset matcher 230 generates the matchcount table 500 based on calculating a number of valid hash counts foreach matching process. For example, the asset matcher 230 compares thesignatures in the MDA 1 302 to the hash table 400. As depicted in thematch count table 500, five signatures in the MDA 1 302 match the MDA 3306 and seven signatures in the MDA 1 302 match the MDA 4 308. Inanother example, the asset matcher 230 compares the signatures in theMDA 3 306 to the hash table 400. As depicted in the match count table500, five signatures in the MDA 3 306 match the MDA 1 302 and sixsignatures in the MDA 3 306 match the MDA 4 308.

In the illustrated example, the asset matcher 230 generates the matchpercentage table 502 based on the match count table 500. In theillustrated example of table 502, the asset matcher 230 calculates amatching percentage of 62.5% when comparing the signatures of the MDA 1302 to the MDA 3 306. For example, the asset matcher 230 calculates avalid hash count of five based on determining that the signatures A1,A3, A4, A5, and A6 of the MDA 1 302 match the signatures C1, C3, C4, C5,and C6 of the MDA 3 306. In such an example, the asset matcher 230calculates a matching percentage of 62.5% based on the valid hash countwith respect to the total number of signatures (e.g., 5 valid hashcounts÷8 total signatures=62.5%).

In another example, the asset matcher 230 calculates a valid hash countof seven based on determining that the signatures A1, A2, A3, A4, A5,A6, and A8 of the MDA 1 302 match the signatures D1, D2, D3, D4, D5, D6,and D8 of the MDA 4 308. In such an example, the asset matcher 230calculates a matching percentage of 87.5% based on the valid hash countwith respect to the total number of signatures (e.g., 7 valid hashcounts÷8 total signatures=87.5%).

In some examples, the asset grader 240 assigns, generates, etc., a gradefor each of the MDA 1 302, the MDA 3 306, and the MDA 4 308 based on thematch count table 500, the match percentage table 502, etc. For example,the asset grader 240 may assign a higher grade to the MDA 4 308 based onthe MDA 4 308 having a total valid hash count of 13 (e.g., 7 valid hashcounts compared to MDA 1 302+6 valid hash counts compared to MDA 3306=13 total valid hash counts), which is greater than a total validhash count of 12 for the MDA 1 302 and a total valid hash count of 11for the MDA 3 306.

In another example, the asset grader 240 may assign a higher grade tothe MDA 4 308 based on the MDA 1 302 and the MDA 1 306 having a highermatch percentage to MDA 4 308 than any other MDA. For example, the MDA 1302 matches the MDA 4 308 the best with a match percentage of 87.5%while only matching the MDA 3 306 with a match percentage of 62.5%. Inanother example, the MDA 3 306 matches the MDA 4 308 the best with amatch percentage of 85.7% while only matching the MDA 1 302 with a matchpercentage of 62.5%. As a result, the example asset grader 240 mayidentify the MDA 4 308 as the best candidate based on multiple MDAmatching the MDA 4 308 the best.

In some examples, the asset grader 240 generates a grade based ondetermining a matched duration. For example, the asset grader 240 maydetermine that the MDA 4 308 includes eight signatures that match atleast one signature in another MDA. For example, the asset grader 240may determine that the signatures D1, D2, D3, D4, D5, D6, D8, and D9match at least one of the signatures included in the MDA 1 302 and theMDA 3 306. In such an example, the asset grader 240 may calculate thematched duration to be eight time units corresponding to the eightsignatures that match at least one other signature in another MDA.

In some examples, the asset grader 240 generates a grade based oncalculating a difference between an expected duration of media and amatched duration of the media. For example, the asset grader 240 maydetermine that the matched duration of the MDA 4 308 is eight time unitsfor media. In such an example, the asset grader 240 may determine thatan expected duration of the media is ten time units. In such an example,the asset grader 240 may calculate a difference between the expectedduration and the matched duration to be two time units (e.g., ten timeunits corresponding to the expected duration−eight time unitscorresponding to the matched duration=two time units, etc.). In responseto calculating the difference, the example asset grader 240 may comparethe difference to a threshold, and determine whether the differencesatisfies the threshold (e.g., the difference is less than two timeunits, less than four time units, etc.). In such an example, the assetgrader 240 may determine that the difference of two time units is lessthan an example threshold of three time units and, thus, satisfies thethreshold.

Flowcharts representative of example machine readable instructions forimplementing the MDAM 118 of FIGS. 1-2 are shown in FIGS. 6-8. In theseexamples, the machine readable instructions comprise a program forexecution by a processor such as the processor 912 shown in the exampleprocessor platform 900 discussed below in connection with FIG. 9. Theprogram may be embodied in software stored on a non-transitory computerreadable storage medium such as a CD-ROM, a floppy disk, a hard drive, adigital versatile disk (DVD), a Blu-ray disk, or a memory associatedwith the processor 912, but the entire program and/or parts thereofcould alternatively be executed by a device other than the processor 912and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in FIGS. 6-8, many other methods of implementing the exampleMDAM 118 may alternatively be used. For example, the order of executionof the blocks may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally or alternatively, any orall of the blocks may be implemented by one or more hardware circuits(e.g., discrete and/or integrated analog and/or digital circuitry, aField Programmable Gate Array (FPGA), an Application Specific Integratedcircuit (ASIC), a comparator, an operational-amplifier (op-amp), a logiccircuit, etc.) structured to perform the corresponding operation withoutexecuting software or firmware.

As mentioned above, the example processes of FIGS. 6-8 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim lists anythingfollowing any form of “include” or “comprise” (e.g., comprises,includes, comprising, including, etc.), it is to be understood thatadditional elements, terms, etc. may be present without falling outsidethe scope of the corresponding claim. As used herein, when the phrase“at least” is used as the transition term in a preamble of a claim, itis open ended in the same manner as the term “comprising” and“including” are open ended.

FIG. 6 is a flowchart representative of an example method 600 that maybe performed by the example MDAM 118 of FIGS. 1-2 to identify a mediadevice asset to be loaded into a media device asset database for AMEmeasurement and/or reporting. The example method 600 begins at block 602when the example MDAM 118 obtains media device asset(s). For example,the network interface 200 may obtain one or more media device assetsfrom the media devices 102, 104, 106 of FIG. 1.

At block 604, the example MDAM 118 identifies database candidate(s). Forexample, the asset quality evaluator 210 may identify the MDA 1 302, theMDA 3 306, and the MDA 4 308 of FIG. 3 to be database candidate assetsbased on not being identified as disqualified media device assets. Insuch an example, the asset quality evaluator 210 may discard the MDA 2304 and the MDA 5 310 based on being identified disqualified mediadevice assets. At block 606, the example MDAM 118 generates a hashtable. For example, the asset hasher 220 may generate the hash table 400of FIGS. 4-5 based on applying one or more hashing algorithms to the MDA1 302, the MDA 3 306, and the MDA 4 308.

At block 608, the example MDAM 118 compares the database candidate(s) tothe hash table. For example, the asset matcher 230 may compare the MDA 1302, the MDA 3 306, and the MDA 4 308 to the hash table 400. At block610, the example MDAM 118 grades the database candidate(s). For example,the asset grader 240 may rank, grade, etc., the MDA 1 302, the MDA 3306, and the MDA 4 308 based on the match count table 500, the matchpercentage table 502, etc.

At block 612, the example MDAM 118 identifies a database candidate forloading into a database. For example, the asset grader 240 may identifythe MDA 4 308 as a reference media device asset to be stored into thedatabase 260 for AME measurement and/or reporting.

At block 614, the example MDAM 118 processes the identified databasecandidate. For example, the asset loader 250 may trim, crop, etc., thenon-matching portions of the MDA 4 308. In such an example, the assetloader 250 may not remove any portion of the MDA 4 308 based on eachsignature of the MDA 4 308 matching at least one other signature of theMDA 1 302, the MDA 3 304, etc. Alternatively, the example asset loader250 may remove the second signature D2 of the MDA 4 308 based on thesecond signature D2 only matching one and not both of the MDA 1 302 andthe MDA 304 (e.g., a signature that does not match all other candidatesmay be trimmed, cropped, etc.).

At block 616, the example MDAM 118 loads the identified databasecandidate into the database. For example, the asset loader 250 may loadthe MDA 4 308 into the database 260 to be used as a reference mediadevice asset for AME measurement and/or reporting.

Additional detail in connection with obtaining media device asset(s)(FIG. 6, block 602) is shown in FIG. 7. FIG. 7 is a flowchartrepresentative of an example method 700 that may be performed by theexample MDAM 118 of FIGS. 1-2 to identify one or more media deviceassets to process for media device asset qualification. The examplemethod 700 begins at block 702 when the example MDAM 118 obtains a mediadevice asset from a media device. For example, the network interface 200may obtain the media device asset 112 of FIG. 1 from the media device102 of FIG. 1 via the network 120 of FIG. 1. In another example, thenetwork interface 200 may obtain the MDA 1 302, the MDA 2 304, the MDA 3306, the MDA 4 308, and/or the MDA 5 310 of FIG. 3 from the mediadevices 102, 104, 106 of FIG. 1.

At block 704, the example MDAM 118 determines whether the media deviceasset is a duplicate syndicated media device asset. For example, theasset quality evaluator 210 may compare the media device asset 112 ofFIG. 1 to one or more media device assets in the database 260 of FIG. 2.In such an example, the asset quality evaluator 210 may determine thatthe media device asset 112 of FIG. 1 is a duplicate syndicated mediadevice asset based on matching a syndicated media device asset in thedatabase 260.

If, at block 704, the example MDAM 118 determines that the media deviceasset is not a duplicate syndicated media device asset, control proceedsto block 710 to determine whether the media device asset is a duplicateproprietary media asset.

If, at block 704, the example MDAM 118 determines that the media deviceasset is a duplicate syndicated media device asset, then, at block 706,the MDAM 118 increments a counter corresponding to a syndicated mediadevice asset. For example, the asset quality evaluator 210 may incrementa duplicate syndicated media device asset counter based on the mediadevice asset 112 matching a syndicated media device asset in thedatabase 260.

At block 708, the example MDAM 118 discards the media device asset. Forexample, asset quality evaluator 210 may discard the media device asset112 of FIG. 1 when the asset quality evaluator 210 increments theduplicate syndicated media device asset counter.

At block 710, the example MDAM 118 determines whether the media deviceasset is a duplicate proprietary media asset. For example, the assetquality evaluator 210 may compare the media device asset 112 of FIG. 1to the database 260. In such an example, the asset quality evaluator 210may determine that the media device asset 112 of FIG. 1 is a duplicateproprietary media asset based on matching a proprietary media asset inthe database 260.

If, at block 710, the example MDAM 118 determines that the media deviceasset is not a duplicate proprietary media asset, control proceeds toblock 716 to determine whether the media device asset is a syndicatedduplicate of a proprietary media asset.

If, at block 710, the example MDAM 118 determines that the media deviceasset is a duplicate proprietary media asset, then, at block 712, theMDAM 118 increments a counter corresponding to a proprietary mediaasset. For example, the asset quality evaluator 210 may increment aduplicate proprietary media asset counter based on the media deviceasset 112 matching a proprietary media asset in the database 260.

At block 714, the example MDAM 118 discards the media device asset. Forexample, asset quality evaluator 210 may discard the media device asset112 of FIG. 1 when the asset quality evaluator 210 increments theduplicate proprietary media asset counter.

At block 716, the example MDAM 118 determines whether the media deviceasset is a syndicated duplicate of a proprietary media asset. Forexample, the asset quality evaluator 210 may compare the media deviceasset 112 of FIG. 1 to the database 260. In such an example, the assetquality evaluator 210 may determine that the media device asset 112 ofFIG. 1 is a syndicated duplicate (e.g., obtained from a media device,etc.) of a proprietary asset based on the media device asset 112matching a proprietary media asset in the database 260.

If, at block 716, the example MDAM 118 determines that the media deviceasset is not a syndicated duplicate of a proprietary media asset,control proceeds to block 720 to determine whether to continuemonitoring the media device.

If, at block 716, the example MDAM 118 determines that the media deviceasset is a syndicated duplicate of a proprietary media asset, then, atblock 718, the MDAM 118 replaces a proprietary media asset with themedia device asset. For example, the asset quality evaluator 210 mayreplace a proprietary media asset stored in the database 260 with themedia device asset 112 of FIG. 1 when the media device asset 112 matchesa proprietary media asset stored in the database 260.

At block 720, the example MDAM 118 determines whether to continuemonitoring the media device. For example, the network interface 200 maydetermine that the media devices 102, 104, 106 of FIG. 1 are no longerpresenting the media 108 of FIG. 1.

If, at block 720, the example MDAM 118 determines to continue monitoringthe media device, control returns to block 702 to obtain another mediadevice asset from the media device. If, at block 720, the example MDAM118 determines not to continue monitoring the media device, then, atblock 722, the MDAM 118 identifies media device assets to process. Forexample, the asset quality evaluator 210 may identify the media deviceasset 112 of FIG. 1 to undergo media device asset qualification. Forexample, the asset quality evaluator 210 may identify the media deviceasset 112 to undergo media device asset qualification when the assetquality evaluator 210 determines that the media device asset 112 is notone of a duplicate syndicated media device asset, a duplicateproprietary media asset, or a syndicated duplicate of a proprietarymedia asset. For example, the media device asset 112 may not be in thedatabase 260. In another example, the media device asset 112 maycorrespond to a database candidate stored in a temporary database.

Additional detail in connection with identifying database candidate(s)(FIG. 6, block 604) is shown in FIG. 8. FIG. 8 is a flowchartrepresentative of an example method 800 that may be performed by theexample MDAM 118 of FIGS. 1-2 to identify one or more media deviceassets as a database candidate to entered into a media device assetdatabase for AME measurement and/or reporting. The example method 800begins at block 802 when the example MDAM 118 selects a media deviceasset of interest to process. For example, the asset quality evaluator210 may select the MDA 1 302 of FIG. 3 to process.

At block 804, the example MDAM 118 selects a language to process. Forexample, the asset quality evaluator 210 may select the English languageto process the signatures A1-A8 of the MDA 1 302.

At block 806, the example MDAM 118 determines whether media identifiersindicate a correct language to process. For example, the asset qualityevaluator 210 may determine that the media identifiers AA1-AA8 of theMDA 1 302 indicate that the signatures A1-A8 of the MDA 1 302 areEnglish-based signatures. As a result, the example asset qualityevaluator 210 may determine that the media identifiers AA1-AA8 indicatethat the correct language is being processed for the correspondingsignatures A1-A8 of the MDA 1 302.

If, at block 806, the example MDAM 118 determines that the mediaidentifiers do not indicate the correct language to process, controlproceeds to block 816 to identify the selected MDA as a disqualifiedMDA. If, at block 806, the example MDAM 118 determines that the mediaidentifiers indicate the correct language to process, then, at block808, the MDAM 118 determines whether the media identifiers indicatetrick mode.

At block 808, the example MDAM 118 determines whether the mediaidentifiers indicate trick mode. For example, the asset qualityevaluator 210 may determine that one or more of the media identifiersAA1-AA8 of the MDA 1 302 indicate trick mode based on media-identifyingmetadata in the one or more media identifiers AA1-AA8.

If, at block 808, the example MDAM 118 determines that the mediaidentifiers indicate trick mode, control proceeds to block 816 toidentify the selected MDA as a disqualified MDA. If, at block 808, theexample MDAM 118 determines that the media identifiers do not indicatetrick mode, then, at block 810, the MDAM 118 determines whether themedia identifiers indicate a minimum duration. For example, the assetquality evaluator 210 may determine that the MDA 1 302 has a duration ofeight time units based on the PAS timestamps included in the mediaidentifiers AA1-AA8 of FIG. 3, where the duration is greater than anexample minimum duration threshold of five time units.

If, at block 810, the example MDAM 118 determines that the mediaidentifiers do not indicate a minimum duration, control proceeds toblock 816 to identify the selected MDA as a disqualified MDA. If, atblock 810, the example MDAM 118 determines that the media identifiers doindicate a minimum duration, then, at block 812, the MDAM 118 selects amedia identifier of interest to process in the selected MDA. Forexample, the asset quality evaluator 210 may select the media identifierAA1 of the MDA 1 302 of FIG. 3.

At block 814, the example MDAM 118 determines whether the selected mediaidentifier indicates a continuity anomaly. For example, the assetquality evaluator 210 may compare a first PAS timestamp corresponding tothe media identifier AA1 of the MDA 1 302 and determine whether a secondtimestamp corresponding to the media identifier AA2 of the MDA 1 302precedes the media identifier AA1. In such an example, if the secondtimestamp precedes the first timestamp, then the example asset qualityevaluator 210 may determine that there is a jump in PAS timestamps(e.g., a viewing operation such as a rewind, fast-forward, etc., mayhave occurred, etc.).

If, at block 814, the example MDAM 118 determines that the selectedmedia identifier does not indicate a continuity anomaly, controlproceeds to block 820 to select another media identifier of interest.If, at block 814, the example MDAM 118 determines that the selectedmedia identifier does indicate a continuity anomaly, then, at block 816,the MDAM 118 identifies the selected MDA as a disqualified MDA. Forexample, the asset quality evaluator 210 may identify the MDA 5 310based on the media identifiers E1-E4 indicating an incorrect language toprocess, trick mode, not satisfying a minimum duration threshold, etc.

At block 818, the example MDAM 118 removes the disqualified MDA from alist to process. For example, the asset quality evaluator 210 may removethe MDA 2 304 and/or the MDA 5 310 from a list to process based when theMDA 2 304 and/or the MDA 5 310 are identified as disqualified MDAs.

At block 820, the example MDAM 118 determines whether there is anothermedia identifier of interest to process in the selected MDA. Forexample, the asset quality evaluator 210 may determine that the mediaidentifiers AA3-AA8 of the MDA 1 302 have not yet been processed afterprocessing the media identifiers AA1-AA2 of the MDA 1 302.

If, at block 820, the example MDAM 118 determines that there is anothermedia identifier of interest to process in the selected MDA, controlreturns to block 812 to select another media identifier of interest toprocess in the selected MDA. If, at block 820, the example MDAM 118determines that there is not another media identifier of interest toprocess in the selected MDA, then, at block 822, the MDAM 118 determineswhether there is another MDA of interest to process. For example, theasset quality evaluator 210 may determine that the MDA 2 304, the MDA 3306, the MDA 4 308, and/or the MDA 5 310 have not yet been processedafter processing the MDA 1 302 of FIG. 3.

If, at block 822, the example MDAM 118 determines that there is anotherMDA of interest to process, control returns to block 802 to selectanother MDA of interest to process. If, at block 822, the example MDAM118 determines that there is not another MDA of interest to process,then, at block 824, the MDAM 118 generates a list of databasecandidates. For example, the asset quality evaluator 210 may generate alist of database candidates including the MDA 1 302, the MDA 3 306, andthe MDA 4 308 based on not being identified as a disqualified MDA. Insome examples, the asset quality evaluator 210 may generate a list thatdoes not include any database candidates. For example, the asset qualityevaluator 210 may identify each MDA of interest to be a disqualifiedMDA. In response to generating a list of database candidates, theexample method 800 concludes.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions of FIGS. 6-8 to implement the MDAM 118 ofFIGS. 1-2. The processor platform 900 can be, for example, a server, apersonal computer, or any other type of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer. The hardware processor may be asemiconductor based (e.g., silicon based) device. In this example, theprocessor 912 implements the example asset quality evaluator 210, theexample asset hasher 220, the example asset matcher 230, the exampleasset grader 240, and the example asset loader 250.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface. Theinterface circuit 920 implements the example network interface 200.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and/or commands into the processor 912. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 920 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip and/or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives. The example massstorage device 928 implements the example database 260.

The coded instructions 932 of FIGS. 6-8 may be stored in the massstorage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable non-transitory computer readablestorage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that identifymedia device assets for AME measurement and/or reporting based onobtaining media device assets from a plurality of media devices. Byidentifying a media device asset for AME measurement and/or reportingbased on processing multiple media device assets, an AME can improveavailable memory storage by storing a reduced number of media deviceassets. Moreover, by identifying disqualified media device assets, theAME can improve memory and processor utilization (e.g., increaseavailable memory storage and/or calculation resources) due to performingmedia device asset qualification on a fewer number of databasecandidates.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. An apparatus comprising: an asset hasher to generate a hash tableusing candidate media device assets generated by media devices duringrespective presentations of media at the media devices, the candidatemedia device assets including a signature and a media identifier thatidentifies the media, wherein the media does not have a referencesignature in a reference database; an asset matcher to calculate one ormore counts of matches of A) signature and a media identifier of a firstone of the candidate media device assets and B) respective signaturesand media identifiers of multiple ones of the remaining candidate mediadevice assets using the hash table; an asset grader to, after the one ormore counts of matches are calculated, identify the signature of thefirst one of the candidate media device assets as the referencesignature based on the one or more counts of matches; and an assetloader to load the reference signature into the reference database afteridentifying the signature of the first one of the candidate media deviceassets as the reference signature.
 2. The apparatus of claim 1, furtherincluding an asset quality evaluator to disqualify one or more of thecandidate media device assets when a jump in a timestamp is identified.3. The apparatus of claim 1, further including an asset qualityevaluator to one or more candidate media device assets when acorresponding media identifier indicates an incorrect language.
 4. Theapparatus of claim 1, further including an asset quality evaluator toone or more candidate media device assets when the one or more candidatemedia device assets fail to satisfy a minimum duration threshold. 5.(canceled)
 6. The apparatus of claim 1, wherein the asset grader is tograde the candidate media device assets based on a difference between anexpected duration of the media and a matched duration of the media, thematched duration corresponding to a number of signatures in the firstone of the candidate media device assets that matches a signature in atleast one other candidate media device asset.
 7. A method comprising:generating a hash table using candidate media device assets generated bymedia devices during respective presentations of media at the mediadevices, the candidate media device assets including a signature and amedia identifier that identifies the media, wherein the media does nothave a reference signature in a reference database; calculating one ormore counts of matches of A) a signature and a media identifier of afirst one of the candidate media device assets and B) respectivesignatures and media identifiers of multiple ones of the remainingcandidate media device assets using the hash table; identifying, afterthe one or more counts of matches are calculated, the signature of thefirst one of the candidate media device assets as the referencesignature based on the one or more counts of matches; and loading thereference signature into the reference database after identifying thesignature of the first one of the candidate media device assets as thereference signature.
 8. The method of claim 7, further includingdisqualifying one or more of the candidate media device assets when ajump in a timestamp is identified.
 9. The method of claim 7, furtherincluding disqualifying one or more candidate media device assets when acorresponding media identifier indicates an incorrect language.
 10. Themethod of claim 7, further including disqualifying one or more candidatemedia device assets when the one or more candidate media device assetsfail to satisfy a minimum duration threshold.
 11. (canceled) 12.(canceled)
 13. The method of claim 7, further including calculating adifference between an expected duration of the media and a matchedduration of the media, the matched duration corresponding to a number ofsignatures in the first one of the candidate media device assets thatmatches a signature in at least one other candidate media device asset.14. A non-transitory computer readable storage medium comprisinginstructions which, when executed, cause a machine to at least: generatea hash table using candidate media device assets generated by mediadevices during respective presentations of media at the media devices,the candidate media device assets including a signature and a mediaidentifier that identifies the media, wherein the media does not have areference signature in a reference database; calculate one or morecounts of matches of A) a signature and a media identifier of a firstone of the candidate media device assets and B) respective signaturesand media identifiers of multiple ones of the remaining candidate mediadevice assets using the hash table; identify the signature of the firstone of the candidate media device assets as the reference signaturebased on the one or more counts of matches; and load the referencesignature into the reference database after identifying the signature ofthe first one of the candidate media device assets as the referencesignature.
 15. The non-transitory computer readable storage medium ofclaim 14, further including instructions which, when executed, cause themachine to at least disqualify one or more candidate media device assetswhen a jump in a timestamp is identified.
 16. The non-transitorycomputer readable storage medium of claim 14, further includinginstructions which, when executed, cause the machine to at leastdisqualify one or more candidate media device assets when acorresponding media identifier indicates an incorrect language.
 17. Thenon-transitory computer readable storage medium of claim 14, furtherincluding instructions which, when executed, cause the machine to atleast disqualify one or more candidate media device assets when the oneor more candidate media device assets fail to satisfy a minimum durationthreshold.
 18. (canceled)
 19. (canceled)
 20. The non-transitory computerreadable storage medium of claim 14, further including instructionswhich, when executed, cause the machine to at least calculate adifference between an expected duration of the media and a matchedduration of the media, the matched duration corresponding to a number ofsignatures in the first one of the candidate media device assets thatmatches a signature in at least one other candidate media device asset.21. The apparatus of claim 1, further including an asset qualityevaluator to identify the candidate media device assets by: obtainingdatabase candidates including a signature, the database candidatesincluding first database candidates having the media identifier; storingthe first database candidates in a database different from the referencedatabase when the media identified by the media identifier does not havethe reference signature in the reference database; incrementing acounter when each of the first database candidates are stored in thedatabase; and identifying the first database candidates as the candidatemedia device assets when the counter satisfies a threshold.
 22. Theapparatus of claim 21, further including a network interface to obtainthe database candidates from the media devices via a network.
 23. Themethod of claim 7, further including: obtaining database candidatesincluding a signature, the database candidates including first databasecandidates having the media identifier; storing the first databasecandidates in a database different from the reference database when themedia identified by the media identifier does not have the referencesignature in the reference database; incrementing a counter when each ofthe first database candidates are stored in the database; andidentifying the first database candidates as the candidate media deviceassets when the counter satisfies a threshold.
 24. The non-transitorycomputer readable storage medium of claim 14, further includinginstructions which, when executed, cause the machine to at least: obtaindatabase candidates including a signature, the database candidatesincluding first database candidates having the media identifier; storethe first database candidates in a database different from the referencedatabase when the media identified by the media identifier does not havethe reference signature in the reference database; increment a counterwhen each of the first database candidates are stored in the database;and identify the first database candidates as the candidate media deviceassets when the counter satisfies a threshold.
 25. The non-transitorycomputer readable storage medium of claim 24, further includinginstructions which, when executed, cause the machine to obtain thedatabase candidates from the media devices via a network.